WatsonPaths: Scenario-Based Question Answering and Inference over Unstructured Information
| Autor: | J. William Murdock, Siddharth Patwardhan, Aditya Kalyanpur, David A. Ferrucci, Sugato Bagchi, Jennifer Chu-Carroll, Erik T. Mueller, Michael A. Barborak, John M. Prager, Michael R. Glass, David W. Buchanan, Adam Lally |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Information retrieval Watson Computer science Inference 02 engineering and technology Set (abstract data type) 03 medical and health sciences 030104 developmental biology Medical test Artificial Intelligence Ask price 0202 electrical engineering electronic engineering information engineering Question answering Graph (abstract data type) 020201 artificial intelligence & image processing Graphical model |
| Zdroj: | AI Magazine; Vol 38, No 2: Summer 2017; 59-76 |
| ISSN: | 2371-9621 0738-4602 |
| DOI: | 10.1609/aimag.v38i2.2715 |
| Popis: | We present WatsonPaths, a novel system that can answer scenario-based questions. These include medical questions that present a patient summary and ask for the most likely diagnosis or most appropriate treatment. WatsonPaths builds on the IBM Watson question answering system. WatsonPaths breaks down the input scenario into individual pieces of information, asks relevant subquestions of Watson to conclude new information, and represents these results in a graphical model. Probabilistic inference is performed over the graph to conclude the answer. On a set of medical test preparation questions, WatsonPaths shows a significant improvement in accuracy over multiple baselines. |
| Databáze: | OpenAIRE |
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